Recommended Systems using Collaborative Filtering and Classification Algorithms in Data Mining Dhwani Shah 2008A7PS097G Mentor – Mrs. Shubhangi Gawali BITSC331 2011 1 BITS – Pilani‚ K.K Birla Goa INDEX S. No. 1. 2. 3. 4. 5. 6. 7. 8. 9. Topic Introduction to Recommended Systems Problem Statement Apriori Algorithm Pseudo Code Apriori algorithm Example Classification Classification Techniques k-NN algorithm Determine a good value of k References Page No. 3 5 5 7 14 16 19 24 26 2
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prediction rate. Data mining objectives: I would like to explore the pre conceived ideas I have about the sinking of the titanic‚ and prove if they are correct. Was there a majority of 3rd class passengers who died? What was the ratio of passengers who died‚ male or female? Did the location of cabins make a difference as to who survived? Did chivalry ring through and did ‘women and children first’ actually happen? Data Understanding: Describe the data: Figure Class
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regression model to testing and validation dataset (output is in “LR_Output2”‚ “LR_Testscore2”‚ and “LR_ValidLiftChart2”). In testcore sheet‚ we can see the probability output we generated for each row from test data. Below shows the regression model and scoring summary. 3. a) the data of purchaser only is in “Purchasers_only” sheet b) Partition is shown in “Data_Partition2” sheet c) Multiple Linear regression output can be seen in “MLR_Output1”. Target variable is “spending”. We select every
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Title: “Data Mining: The Mushroom Database” Author: Hemendra Pal Singh* In this review “Data Mining: The Mushroom Database” is focuses in the study of database or datasets of a mushroom. The purpose of the research is to broaden the preceding researches by administer new data sets of stylometry‚ keystroke capture‚ and mouse movement data through Weka. Weka stands for Waikato environment for knowledge analysis‚ and it is a popular suite of machine learning software written in Java‚ developed at
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Introduction to Data Mining Summer‚ 2012 Homework 3 Due Monday June.11‚ 11:59pm May 22‚ 2012 In homework 3‚ you are asked to compare four methods on three different data sets. The four methods are: • Indicator Response Matrix Linear Regression to the Indicator Response Matrix. You need to implement the ridge regression and tune the regularization parameter. The material of this algorithm can be found in Page 103 to Page 106 in the book ”The Elements of Statistical Learning” (http://www-stat
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Mid Term Exam 15.062 Data Mining Problem 1 (25 points) For the following questions please give a True or False answer with one or two sentences in justification. 1.1 A linear regression model will be developed using a training data set. Adding variables to the model will always reduce the sum of squared residuals measured on the validation set. 1.2 Although forward selection and backward elimination are fast methods for subset selection in linear regression‚ only step-wise selection is guaranteed
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BUILDING A BUSINESS MODEL ON DATA WAREHOUSING FOUNDATIONS: Executive Summary mySupermarket is a grocery shopping and comparison website which aims to provide customers with the best price for their shopping. This report examines how data warehousing provided mySupermarket with the foundation in which to build a successful enterprise‚ and allowed a subsequent expansion into the ‘business intelligence’ sector. The research draws attention to the problems and limitations that mySupermarket
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Systems The goal of the term project is to develop a useful and viable prediction or classification model based on data. You will need to develop a research question‚ which you refine further based on the availability of data. You may need to merge multiple data sets together. Process: • Each team of 2 or 3 students will work on a business problem involving data analysis with real data. The project will focus on classification and prediction methods we covered during the semester. • A presentation
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Assignment : Data Mining Student : Mohamed Kamara Professor : Dr. Albert Chima Dominic Course : CIS 500- Information Systems for Decision Making Data : 06/11/2014 This report is an analysis of the benefits of data mining to business practices
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Excellence for Data Mining in Egypt By: Aref Rashad I- Introduction The convergence of computer resources connected via a global network has created an information tool of unprecedented power‚ a tool in its infancy. The global network is awash with data‚ uncoordinated‚ unexplored‚ but potentially containing information and knowledge of immense economic and technical significance. It is the role of data mining technologies arising from many discipline areas to convert that data into information
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